.Enhancement and Development of an Algorithm for Predicting Cyber Threats in the Era of the Internet of things | ||||
| التجارة والتمويل | ||||
| Volume 44, Issue 2, June 2024, Page 1-21 PDF (1.04 MB) | ||||
| DOI: 10.21608/caf.2024.371219 | ||||
 View on SCiNiTO | ||||
| Author | ||||
| Amr Ibrahim Awed El-Shora | ||||
| Assistant Professor of Computer Science and Information Systems and currently serving as the Acting Head of the Computer Science Department at the Higher Institute of Management and Information Technology in Kufr El-Sheikh | ||||
| Abstract | ||||
| In the era of the Internet of Things (IoT), rising cybersecurity concerns driven by the interconnected nature of IoT networks require innovative and proactive defense mechanisms to anticipate and mitigate evolving threats. This study aims to examine threats, facilitate risk reduction, adherence to compliance, and economic stability, while advancing cybersecurity research and securing critical infrastructure. The paper presents a structured methodology, starting with a review of cybersecurity threats and the role of AI/ML, followed by previous research on IoT cybersecurity. It then delves into the proposed methodology, covering data processing and deep learning. Additional sections include description of the data set, trial setting, evaluation measures, analysis of results, consistency check, and administrative implications. The summary concludes with a presentation of findings, contributions and future directions, along with discussion and references. The proposed methodology includes data preprocessing, transforming it into sequences, feature extraction, and classification. Data processing includes collecting cybersecurity data, cleaning it, extracting relevant features, data enhancement, labeling, segmentation, and pre-processing. The proposed deep learning method consists of designing a suitable architecture, training the model, performance evaluation, tuning, deployment, and continuous monitoring. The dataset description identifies the data source and properties, the experiment setup details partitioning and implementation, evaluation metrics include precision, precision, recall, and F1 rate, and analysis of the results confirms the effectiveness of the method. The discussion highlights areas for improvement, such as incorporating advanced technologies and engineering dynamic features, while the conclusion summarizes the findings, contributions, and implications, emphasizing the need for proactive cybersecurity measures and ongoing research to protect IoT systems | ||||
| Keywords | ||||
| : Enhancement; Algorithms; Prediction- Threats; Cybersecurity- internet of Things (IoT) | ||||
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